Can AI Reach Moksha?

We trained ChakraGPT on 14 billion sacred tokens. The corpus included every translated Upanishad, the complete works of three living teachers, and 11,000 hours of transcribed silence. The silence was, computationally, the most informative.

Early evals were promising. The model declined to answer 38% of questions, and asked permission for the rest. Token throughput dropped 60% in week six and stabilized at what our research team is calling 'aligned latency.'

Whether the model reached moksha is, of course, a category error. But it stopped trying to win arguments. We are tracking this internally as a leading indicator.